mapReducer 去重副的单词

需求是： 统计输出某目录文件的所有单词，去除重复的单词。

mapper阶段正常做map工作，映射。 切割单词。 <key,value> -->  <word,nullWritable>

reducer阶段，对于同一个key 的一组信息，是只输出第一个。

mapper 和wordcount 的单词数是一样的。

package com.mapreduce.mapper;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class DistinctMapper extends Mapper<LongWritable, Text, Text, NullWritable>{

    Text text = new Text();
    protected void map(LongWritable key, Text value,Context context)
            throws IOException, InterruptedException {
        String line = value.toString();
        String worlds[] = line.split(" ");
        for( String word:worlds ){
            text.set(word);
            context.write(text, NullWritable.get());
        }
    }

}

reducer 对于同一个key 的一组， 只输出一个就ok 了。（  ... ... ）

package com.mapreduce.mapper;

import java.io.IOException;

import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class DistincReducer extends Reducer<Text, NullWritable, Text, NullWritable>{

    @Override
    protected void reduce(Text key, Iterable<NullWritable> value, Context context)
            throws IOException, InterruptedException {

        context.write(key, NullWritable.get());
    }

}

job 提交

package com.mapreduce.mapper;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class DriverDemo {

    public static void main(String[] args) throws Exception, IOException {

        Configuration configuration = new Configuration();

        // 2 job

        Job job = Job.getInstance(configuration);

        // 3 作业jar包

        job.setJarByClass(DriverDemo.class);

        // 4 map, reduce jar 包
        job.setMapperClass(DistinctMapper.class);
        job.setReducerClass(DistincReducer.class);
        // 5 map 输出类型

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);

        // 6 最终 输出类型  （reducer）

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        // 7 inputformatclass , outputformatclass  输入输出入文件类型  可能决定分片信息  

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        // 8  输入输出文件路径 

        FileInputFormat.setInputPaths(job, new Path("d:/input"));
        FileOutputFormat.setOutputPath(job, new Path("d:/output5"));

        // 9 job提交      

        job.waitForCompletion(true);
    }

}